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High precision control and deep learning-based corn stand counting algorithms for agricultural robot

arXiv.org Artificial Intelligence

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with $C_{robot}=1.02 \times C_{human}-0.86$ and a correlation coefficient $R=0.96$. The mean relative error given by the algorithm is $-3.78\%$, and the standard deviation is $6.76\%$. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.


Use of artificial intelligence in agriculture

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From cultivation to improving harvesting quality, AI is known as one of the main elements for a surplus yield but that too for the ones who are capable enough to make use of it. Agriculture is seeing rapid adoption of Artificial Intelligence and Machine Learning, both in terms of agricultural products and in field farming techniques. Apart from that, most of the countries are looking forward to involving such techniques. In 2016, the estimated value added by the agricultural industry was estimated at just under 1% of the US GDP. The US Environmental Protection Agency, estimates that agriculture contributes roughly $330 billion in annual revenue to the economy, thus such techniques would definitely speed things up.


Council Post: Artificial Intelligence And Precision Farming: The Dawn Of The Next Agricultural Revolution

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Co-Founder and CTO of Prospera Technologies, leading the company's vision to transform the way food is grown using data science and AI. The human race has come a long way in our ability to produce food at scale. Historian and author Yuval Noah Harari refers to it in his book Sapiens as "an agricultural revolution," using wheat as an example. Ten thousand years ago, wheat was a wild grass that grew in a relatively small region in the Middle East. Today, wheat can be considered one of the most successful plants in history, according to the evolutionary criteria of survival and reproduction. In regions where wheat never existed, such as the Great Plains of North America, you can drive for hundreds of miles without seeing anything else but wheat fields.


AI on the Bog: Monitoring and Evaluating Cranberry Crop Risk

arXiv.org Artificial Intelligence

Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment to facilitate informed decisions that may sustain the economic viability of the farm. Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries. We develop drone-based field data and ground-based sky data collection systems to collect video imagery at multiple time points for use in crop health analysis. Extensive evaluation on the data set shows that it is possible to predict exposed fruit's inner temperature with high accuracy (0.02% MAPE). The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon. With 62.54% mIoU for segmentation and 13.46 MAE for counting accuracies in exposed fruit identification, this system is capable of giving informed feedback to growers to take precautionary action (e.g. irrigation) in identified crop field regions with higher risk of sunburn in the near future. Though this novel system is applied for cranberry health monitoring, it represents a pioneering step forward for efficient farming and is useful in precision agriculture beyond the problem of cranberry overheating.


Blue River Technology Uses Facebook AI For Weed Control

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Artificial intelligence allows farmers to spray weeds while keeping the crop untouched. With crop prices in the dumpster and the world's population growing among a changing climate, artificial intelligence is becoming a life-saving measure for many farmers. From automated planting and harvesting to unmanned vehicles for cultivation and soil sampling, AI has begun to make it more cost efficient for producers to do their job. One of the largest roadblocks is herbicides. According to a 2016 University of Illinois study, the chemical prices are on the rise and pose a big threat to a farmer's bottom line.


Farming Equipment that Can Tell Plant from Weed? It's Already Here (EDITORIAL)

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Automated farming equipment has perhaps never been a hotter topic than right now. Adding fuel to the fire, farm equipment giant John Deere had a big splash at last week's Consumer Electronics Show (CES) in Las Vegas, NV. Last year was a tough act to follow. In 2019, it exhibited its machine learning (ML) and artificial intelligence (AI) enabled S-Series combine. This year, Deere brought out the big guns with its R4038 sprayer.


What Smart Cities Are Learning From Smart Farms

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Cities around the world are getting smarter. Already, street lights in places like San Diego are turning off, and conserving energy, when vehicles and pedestrians aren't around. Soon, connected garbage cans will tell waste haulers when they need to be emptied, optimizing collection routes. Smart buildings will notify maintenance staff of impending repair needs. And parking spots will find you, instead of the other way around.


Machine learning helps plant science turn over a new leaf

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LA JOLLA--(October 7, 2019) Father of genetics Gregor Mendel spent years tediously observing and measuring pea plant traits by hand in the 1800s to uncover the basics of genetic inheritance. Today, botanists can track the traits, or phenotypes, of hundreds or thousands of plants much more quickly, with automated camera systems. Now, Salk researchers have helped speed up plant phenotyping even more, with machine-learning algorithms that teach a computer system to analyze three-dimensional shapes of the branches and leaves of a plant. The study, published in Plant Physiology on October 7, 2019, may help scientists better quantify how plants respond to climate change, genetic mutations or other factors. "What we've done is develop a suite of tools that helps address some common phenotyping challenges," says Saket Navlakha, an associate professor in Salk's Integrative Biology Laboratory and Pioneer Fund Developmental Chair.


Seedo: The Self-Contained Weed Growing Robot

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Powered by AI and Machine Learning technology, Seedo enables anyone to grow anything with no experience and the same amount of space you would need for a mini-fridge. Founded in 2015, the Israeli AgriTech firm's self-contained device generates "high yields of lab-grade, pesticide-free herbs, and vegetables," states Seedo's website. But the company is well aware that the herb that little Seedo will most be responsible for growing, is cannabis. In fact, the device's impressive growing abilities have been translated from the knowledge of the company's founder, retired expert cannabis grower Yaakov Hai. Seedo's biggest market is in the United States where growing and using cannabis recreationally is now legal in 11 states in the USA and in 22 states medical cannabis has been recognised as an effective treatment for numerous health conditions including PTSD, depression, chronic pain and for those undergoing cancer treatment.


Tackling Climate Change with Machine Learning

arXiv.org Artificial Intelligence

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.